This project is trying to investigate if investment in different industry segments would impact on the amount of CO2 emission. Varibales being choosed are CO2 emissions total, CO2 emissions per capita, CO2 emissions per GDP, investment in energy, investment in telecommunication, investment in transportation, investment in water.

Outcomes

• CO2 emissions total
• CO2 emissions per capita
• CO2 emissions per GDP

Predictors

• Investment in energy
• Investment in telecommunication
• Investment in transportation
• Investment in water

## [1] "countrycode" "countryname" "seriescode"  "seriesname"  "SCALE"      
## [6] "Decimals"    "year"        "value"

ggplot(spreadtest,aes(x=countryname,y=CO2total,color=year))+geom_point()+geom_smooth()

ggplot(spreadtest,aes(x=countryname,y=CO2percapita,color=year))+geom_point()+geom_smooth()

plot_ly(selected,x=~as.numeric(as.character(year)),y=~CO2perGDP,type='scatter',mode='lines')

## 
##  Pearson's product-moment correlation
## 
## data:  x and y
## t = 0.52495, df = 109, p-value = 0.6007
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1374618  0.2344165
## sample estimates:
##        cor 
## 0.05021779

## 
##  Pearson's product-moment correlation
## 
## data:  x and y
## t = -0.39648, df = 109, p-value = 0.6925
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2227658  0.1495018
## sample estimates:
##         cor 
## -0.03794859

## 
##  Pearson's product-moment correlation
## 
## data:  x and y
## t = -0.85622, df = 109, p-value = 0.3938
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2641061  0.1062751
## sample estimates:
##         cor 
## -0.08173693